Regulatory trade-off between encouraging the

improvement of technical quality and

recognition of operating and capital costs on

the distribution network operators in Brazil

Samuel J. C. Vieira1,1

, Nivalde de Castro2, Andre L. S. Leite

3

1 ENERGISA.SA – Federal University of Rio de Janeiro – GESEL – COPE,

samueljcvieira@gmail.com

2

Federal University of Rio de Janeiro – GESEL, nivalde@ufrj.br

3

Federal University of Santa Catarina – andre.ls.leite@gmail.com

SESSION: Energy markets and regulation, Regulation and monitoring of energy markets

ABSTRACT

This paper takes the regulatory impact evaluation of the incentive mechanism to improve the

technical quality of electricity distributors in Brazil. The methodology proposed by the Brazilian

regulatory agency (ANEEL) follows the concept of the mechanism RPI - X know by subtracting

the productivity gains in the annual tariff adjustments. Inside the X factor the regulator has

created a mechanism that increases the tariff recognition of companies that can improve the

quality of service. However, this mechanism does not have an empirical model that corroborates

the estimated results and set in a discretionary manner the limits of incentive structure. In this

paper we have created an empirical model that confronts the estimated elasticity percentage to

increase (or decrease) recognition of costs following a panel fixed effects model. In this

statistical model it is possible confront the magnitude of the trade-off in the structure of

regulatory incentives linked to the amount of reconnaissance of operation and capital costs. The

results indicate that in some underlying criteria the tariff recognition is insufficient to offset the

increased costs that ensure the improvement of technical quality in both perspectives:

punishment and incentive recognition for operate with better practices, especially in some

immature concession areas.

KEYWORDS: Regulation, Technical quality, Benchmarking, OPEX, CAPEX.

1 Corresponding author.

1. INTRODUCTION

Since the third periodic cycle revisions of the electricity distribution companies in Brazil, the

firms can obtain tariff gains case deliver a better quality of service to their customers. The

regulatory incentive mechanism adopted is dynamic, ie, greater variations of quality indicators

between two consecutive years will be higher tariff appropriation in the next year.

The current regulation does not provide a model that the incentive level for the quality gains is

empirically estimated. The applied results are derived from assumptions “ad-hoc” limited by the

maximum percentage of recognition of +/- 2% per year. The importance of studying this

mechanism comes from the fact that the strength of regulatory incentive may be underestimated

in two cases: A) the punishment for sell a low quality service, and, B) the reward for improving

services. Both can’t express the degree of aversion of society to low-quality services.

Unfortunately this paper will only be considered the company's vision in terms of costs incurred

to improve the technical quality of the services. An alternative treatment would add to this

approach the vision of consumers against the cost of energy deficit. We don’t treat this approach

on this paper, however is an important perspective for future studies. The hypothesis to be

measure in this paper it will then: “The mechanism of incentives to improve the technical quality

in Brazil corresponds to the opportunity cost of improving the quality of services?”

To answer the question above, we made a regression analysis with panel data, considering two

methodologies: A) The first it is a dynamic panel fixed effects as proposed by ARELLANO and

BOND (1991). We have the intention to address the problem of inertia in trajectory of reducing

costs and connect the estimated incentive to reduce them. With this approach is expected to

obtain statistical significance between the incentive to improve the quality and the cost

trajectories. This case demonstrated insignificant statistical results. B) A second model evaluated

removed the autoregressive term and treat the variation of costs responds only with shocks in the

panel variables. This model considers the “ceteris paribus” vision only on the quality indicators.

This approach proved to be statistical significant. Both models were estimated considering two

stages, where the effects of other variables that affect the costs were treated assuming be

exogenous. The estimated models, therefore, restricted the quality information when separated.

The Premise is not a very strong information that will be compound for a weighted index.

“The results indicate that the regulatory incentive to improve the technical quality of supply

energy is undersized in Brazil”. Besides this introduction the article is divided into the following

sections: 2 – Brazilian model; 3 – Incentive mechanism to improve the quality; 4 – Methodology;

5 – Results; 6 – Conclusions 7 – Conflicts of interest; and 8 – Bibliography.

2. BRASILIAN MODEL

The regulatory and institutional framework of the Brazilian market is based on providing non

verticalized services which generation costs and freight costs – transmission and distribution –

are separated. The latter two characterized by regulated natural monopolies. On the portion of the

distribution revenue acts the incentive mechanism to improve the quality2.

The regulatory model in Brazil turns the operating conditions of the electricity distributors –

provision of infrastructure and quality – in “drivers” of costs and simulates the competitive

market by techniques of “benchmarking”. For this the annual adjustment mechanism of tariffs in

the regulated distribution market is affected by the performance of companies. The Brazilian

mechanism is also one of the most common among regulators in other countries and is the

evaluated joint an inflation index adjustment. The X – factor adjusts the retail tariffs annually3

and this factor is described by

1

As is known in microeconomic theory, the managerial process in competitive markets is different

from that which should be adopted in markets without competition – monopolies – as the

interaction of agents becomes not necessarily cooperative. The mechanism of equation 1 affects

competition in the Brazilian electricity sector and the companies start to compete with each other

seeking higher tariff margin gains.

The X – factor impose compulsory the sharing of productivity on the regulated tariff simulating

the competitive market. It’s magnitude depends on the three factors above on equation 1 that

capture different cost drivers: a) Pd component – or distribution productivity – is responsible for

the sharing of productivity gains on the scale or growth of the concession area, this tends to be

positive, ie, it’s commonly one tariff reducer; b) The T component – or trajectory of operating

costs – is responsible for the equalization of operating processes of distribution, identify and

apply penalties for not achieving the best operation practices. This component tends to be

positive for inefficient firms, also resulting in reduction of tariffs; c) The Q component – is

responsible for regulatory incentive to improve the quality of service. It tends to be negative for

companies that gain efficiency in quality, implicated rate increase when the quality improves,

though it may have an impact on rising costs, causing “trade -off” especially with T component.

2 This portion is known as “portion B” comprises the operation and maintenance costs, and return on investments in

distribution systems.

3 The new indicator that adjusts the tariffs in Brazil is the IPCA - national price index broad consumer - calculated

by the Brazilian Institute of Geography and Statistics. Its adoption will take place from 2016 at concessioners to

renew their contracts. Before the adjustment was made by the IGP-M – general price index of markets.

In competitive markets when the productivity of a business increase this business is more likely

to be successful over time. It can be seen from this that the establishment of regulatory conditions

through the “benchmarking” means the removal of a significant degree of comfort to business

managers and enforce then act searching better practices.

3. INCENTIVE MECANISM TO IMPROVE THE QUALITY

The mechanism to incentive the improvement of the energy quality in Brazil is applied in the

calculation of X – factor according to the sub module 2.5 of PRORET4. The component

responsible for tariff recognition of improvements in quality of service is described by XQ factor

– or simply Q – where improvements or deteriorations in the quality of service provided to

consumers is captured as an increase or reduction of tariffs respectively. For a more detailed

description of this mechanism the main references are the technical notes number 67/2015 –

SRM / SGT / SRD / ANEEL and number 404/2014 – SRE / ANEEL5.

Generally speaking the incentive mechanism for quality improvement to distribution system

operators with more than 60.000 consumer units6 is divided into two components:

A. The incentive component improving the QT (technical quality), and,

B. The incentive component improved QC (commercial quality).

At where:

2

And

3

4

The classification of indicators above is show that, by

4 Tariff Regulation Procedures, available at: http://www.aneel.gov.br/area.cfm?idArea=702. Only in Portuguese,

accessed in14-12-2015.

5 Avaible at; http://www.aneel.gov.br/, Informações Técnicas > Audiências / Consultas > Audiências Públicas >

Audiência Ano 2014 – (Finalizado o período de contribuição em 2015) > Audiência 023/2014, resultados da

primeira e segunda fases.

6 Small distributors have more flexible treatment where the implementation of “Call-centers” and it’s optional

according to the Art. 184 da REN nº 414/2010.

Indicator Definition Regulamentation

DEC Equivalent duration of interruptions per unit consumer (in hours). Module 8 of

PRODIST7

FEC Equivalent frequency of interruptions per unit consumer (in times). Module 8 of

PRODIST7

FER Equivalent frequency of reclamation for each 1000 consumers. REN nº 574/2012

IASC ANEEL index of consumer satisfaction. Results on the degree of consumer satisfaction

with the service provided. -

INS Reflects the answered calls in call-center on respect for calls received less abandoned. REN nº 414/2010

IAb Reflects the list of dropped calls on incoming. REN nº 414/2010

ICO Reflects the ratio of busy calls. REN nº 414/2010

The incentive mechanism for each “driver” of quality has the same quantitative basis. But to

make an evaluation of a new incentive mechanism we are limited by available historical data for

the treatment of the technical component ( ). The commercial component ( ) will not be

considered on this paper for to two reasons: 1 – have recent regulation, and; 2 – the absence of

data for statistical analysis. Anyway, the study of only the technical component corresponds for

70% weight in the estimation of Q component of X – factor.

The regulatory incentive mechanism in the tariff for to improve the DEC (in other words reduce

the indicator) – and therefore the FEC – is given as follows in Brazil:

1. The global indicators8 are computed from adjusting for electrical assembly

9:

2. After identifying the quality indicators set by electrical assembly we building the

individual goals by clustering method using the distance function of the Euclidean

technique, where the benchmarks are defined by clusters of 50 to 100 sets from the

second decile of box-plot dispersion of comparable values.

3. The individual limits leading to overall limits for distributors where the quality goals that

define the pattern of service that being delivered to consumers and are constructed by the

weighted sum of the individual indicators.

4. The distributors are separated in large (market ≥ 1 TWh / year) and small companies.

7 Distribution Procedures, available at em: http://www.aneel.gov.br/area.cfm?idArea=82. Only in Portuguese.

Access in 10-12-2015.

8 The indicator do not consider faults of energy in the transmission system, is the weight sum for each assembly.

9 The electrical assembly is compound for the electric extensions of a determined electric substation and can include

one, many, or only parts of cities.

5. The distributors are divided into two groups in year of analysis: i – companies that meet

the pattern, i.e. manage to overcome the specific quality goals in item 3, and; ii –

companies that don´t meet the pattern:

Distribution operators that

meet the pattern

Distribution operators that

don’t meet the pattern

25 % better

performance75 % remaining

25 % worst

performance75 % remaining

6. Given the two groups of item 4, the distributors are ranked from best to worst effective

performance, and classified in sub-groups (Blue, Green, Red and Purple) performance

where relationships incentive will be more or less restrictive according to the position of

companies in the performance scale compared.

7. Is computed the percentage change between and – two years preceding the

year of evaluation – of DEC (Δ% DEC) and FEC (Δ% FEC), for each distributor on the

case of technical indicators.

8. The calculated value of the percentage change in the index: ( ⁄ ) ,

is applied in the next equations:

Meet the pattern?

Class of performance Band of variation (DEC or FEC) Curve Q(∆I%) in ( % )

Yes 25% better

∆I% ≤ - 25% ( )

-25% < ∆I% < 5% ( )

5% < ∆I% < 20% ( )

∆I% ≥ - 25% ( )

Yes 75% remaining

∆I% ≤ - 25% ( )

-25% < ∆I% < 5% ( )

5% < ∆I% < 20% ( )

∆I% ≥ - 25% ( )

No 75% remaining

∆I% ≤ - 25% ( )

-25% < ∆I% < 5% ( )

5% < ∆I% < 20% ( )

∆I% ≥ - 25% ( )

No 25% worst

∆I% ≤ - 25% ( )

-25% < ∆I% < 5% ( )

5% < ∆I% < 20% ( )

∆I% ≥ - 25% ( )

Finally the values calculated in item 810

are replaced in equations 2, 3 and 4, and applied

directly to the X factor on every revenue portion B of the distributors, leading to higher tariff or

lower level that reflects the quality performance.

4. METHODOLOGY

4.1. Theoretical discussion

The quality of the optimal level adjustment mechanism is based on many factors that are directly

or indirectly on the management of companies. Among the items that most affect the continuity

of energy supply are: i) exogenous factors related to weather – lightning, rain and wind – ii)

building pattern of networks, iii) the incidence of the practice (still common) of power theft iv)

the cost of tariffs, which can increase the level of default by consumers, v) the conflicting

mechanisms of regulatory incentives, especially the requirement to reduce operating costs in

companies with low quality. This last is the object of study of this paper.

The first item mentioned relates to stochastic events of atmospheric nature there are

uncontrollable and unmanageable and generate considerable costs in terms of quality. Added to

this perception the constructive pattern of networks and the degree of depreciation of assets,

increases the system vulnerability. The power theft reduces the operating capacity of enterprises,

leading instability in the supplier. Such factors are condensed on the ability that the

concessionary need to have evaluated the capability to pay of their consumer units. In the

Brazilian case – particular for some concessions that have socially vulnerable areas – there can

be no large proportion of commitment of family income in payment of bills, because which

presses the increase in energy theft and default.

On this paper we estimate of empirical reliably relations that indicate the best possible way what

the average elasticity for the Brazilian market adjustments that converge in the improvement of

quality of service in terms of costs. In a general way these adjustments has the tendency to

evidence part of the information needed so that can identify an optimal level of quality.

From the figure below you can see two behaviors based on the amount of quality indicator. The

first described the red line shows the company's vision, where the indicator increases – lower

quality – lower costs are expended to maintain the service delivery infrastructure. However the

10

Available at: http://www.aneel.gov.br/arquivos/PDF/Proret_Subm%C3%B3dulo%202.5_V2.pdf. 2.5 module of

PRORET, access in 10-12-15.

consumer view is evidenced by the green equation, where the higher the quality – smaller

indicator – more costly it becomes to deliver the service. In this case the energy deficit causes

economic losses to consumers.

IND

Cost

IND*

Cost*

Operation cost

Déficit cost

The dynamic equilibrium between the perception of quality by consumers – cost of the deficit –

and the actual cost of providing a more reliable service – operating cost – define on the middle an

optimal value for the cost for a given concession area.

Generally speaking the red equation can be estimated – and it is about her the study of this article

– from operating data of the distribution companies in Brazil. The green equation has set more

complex because it depends on the consumer market expectations, energy demand growth and

any “trade-offs” involving increase in operating costs, the cost of welfare, opportunity or the

production of products and services that depend on electricity as irreplaceable input.

The simple model presented above shows the importance of following premise: “There is a

theoretical limit of the operating cost of a concession and the level of quality required by its

consumer market.” On this limit none improvement in the quality of service may be appropriate

by the concessionaire or by their consumers, causing economic loses and allocative irrationality.

The most important restriction of the trajectory for the correlation between cost and quality is the

valuation that the consumer units have for the level of quality that are willing to pay.

4.2. Empirical adjustment

Dealt with issues that related to the identification of the efficient cost level compared to quality,

it is necessary to indicate how the cost equations and deficit cost can be calculated. Two

adjustment assumptions were used on this paper. One considers the inertial effect of other

adjustment policies on the level of costs that affect the quality indicators, ie, it was assumed that

the cost depends on the cost today a step forward. The second alternative considers only the

contemporary impact of quality indicators on the level of cost.

The deficit cost equation – the view of consumers – have relatively complex estimation and the

time has not yet formed a consolidated view of how such a relationship could be estimated,

considering the specificities of each concession in Brazil. Some ideas already exist to promote

this adjustment as estimate, using as a proxy GDP growth, compared to the variation of global

indicators for concession. These studies are a continuation of this work, in which it is expected to

give more robustness to the analyzes presented.

The cost equation award for quality can be estimated from operational indicators of distribution

concessions in Brazil. It was decided to adjust the model containing the inertia parameter using

the technique proposed by Arellano and Bond (1991), where the major references on the

construction of mathematical assumptions of analysis can be obtained.

According to GREENE (2008), TRIVEDI and CAMERON (2010) the model proposed by

ARELLANO and BOND (1991) can be described by the following equations:

5

In equation 5 it notes where two equivalent formulations presents part of the information

contained by . In general the dynamic autoregressive term – of interest – is correlated with

the error term, said that the estimators of fixed effects, which depend on second WOOLDRIDGE

(2006) by the transformation of fixed effects will be inconsistent. Thus the model:

( ) ( ) ( ) 6

Becomes inestimable. Since:

( | ) 7

The solution to this problem has been given by applying the generalized moment estimator

proposed by HANSEN (1982), where capture the effect of , leading to consistent

estimator. The OLS estimator is biased in this case because: i) the fixed effects should not be

equal for all companies; ii) the explanatory variables are not exogenous especially – there

is a direct correlation relationship between costs and quality; and iii) the OLS does not allow for

serial correlation in the error term; It’s precisely to correct this autocorrelation which are

included the lagged instruments.

Without this, the set of independent variables included in the model is “all

information” necessary for the estimation of , in this way, any change of dependent variable is

expressed by the impact of “new” information. So it is observed that the effect of temporal

correlation in costs can be “removed” from the model so as to subtract only the effect of

endogenous variables without temporal correlation. However a most suitable inference process

can be built with the exclusion of “inertial” term. In this case the may be included as

exogenous, if the model is not significant, and a model including only the relationship between

the costs and the quality indicator can be estimated by least squares two stages with safety.

According to GREENE (2008) the specification of this model would be:

8

Where it is noted that does not depend on lagged cost . In this case there is no “inertia”

in . The effects of unknown variables not included are treated exogenously.

5. RESULTS

The estimation results of the two models for the DEC and FEC have been calculated from the

data of public audience 023/2014 organized by the National Electric Energy Agency11

(ANEEL).

The number of data set consists only of CAPEX values of the second cycle of periodic revisions

of power distributors extrapolated to the year 2012 for the third cycle results. Thus the data used

in the sample represent a balanced panel from 2005 to 2012, to amount of 61 (of 63) Brazilian

power distributors, totaling a sample of 488 observations.

The estimated models take into account the behavior of costs according to the DEC assumption is

correlated with TOTEX and the FEC is correlated only with the CAPEX, given the particular

nature of each indicator. The DEC depends on the investments and so much of the OPEX, just

watch with the duration of interruptions is strongly related to the time spent for repair of power

outages, and the FEC is particularly dependent on investments so not be affected by OPEX.

The set of considered instruments included the following variables: Market weighted revenue for

each voltage level, kilometers of total network, number of users, FEC performed (DEC

performed when the setting involve FEC), a dummy equal to 1 if the FEC performed is lower

than the target FEC (or equal to 1 is performed DEC is less than the target DEC, in the case of

setting involve FEC). The summary of the adopted models are as follows:

11

See:

http://www.aneel.gov.br/aplicacoes/audiencia/dspListaDetalhe.cfm?attAnoAud=2014&attIdeFasAud=938&id_area=

13&attAnoFasAud=2015 – acessed at 12-12-2015.

i. Relation between the DEC and the TOTEX:

( ) 9

( ) ( ) 10

where:

[ ( ) ] [ ] 11

[ ( ) ]

[ ]

12

ii. Relation between the FEC and the CAPEX:

( ) 13

( ) ( ) 14

Where:

[ ( ) ] [ ] 15

[ ( ) ]

[ ]

16

9 and 13 models are simple panel models, since models 10 and 14 assumes the dynamic

argument proposed by ARELLANO and BOND (1991), where the number of lags was chosen in

this case to be p = 2. Observing these equations, the expected signal to coefficient is negative,

i.e., if the DEC (FEC) increases costs should fall, revealing a negative substitution ratio.

The is an intercept term, can have any signal. If negative, it indicates that business

costs that reach the targets are on average – X % lower than those who do not reach the goal,

revealing that the best quality practices are linked to the best costs practices. If the signal is

positive, it indicates that those with better quality on average operating at a higher cost than the

average, and for this group the average cost is higher. Thus the results are:

Model DEC FEC

Est. Model (9) Model (10) Model (13) Model (14)

L1.TOTEX 0,790***

(0,040)

L1.CAPEX 0,911***

(0,013)

ln(DEC) 0,028 -0,342***

(0,026) (0,065)

ln(FEC) 0,035*** -0,460***

(0,010) (0,125)

lim.DEC 0,011 -0,483***

(0,019) (0,089)

lim.FEC -0,020* -0,743***

(0,010) (0,199)

Cte 2,428*** 13,033*** 0,929*** 12,539***

(0,504) (0,220) (0,157) (0,459)

Prob > F - 0,000 - 0,000

The above results indicate that the models 10 and 14 on both DEC and for FEC are not

significant. That said it is observed that when treated together the term of inertia and quality

indicators lack statistical significance. Therefore it is not possible to estimate the elasticity of

costs when applying a dynamic model, implying that the trajectory of reducing costs and

improving quality indicators not jointly explain the cost level of Brazilian concessions.

One possibility that can’t explain the significance of the dynamic model is the lack of

observations over time. Then it could be biased by the lack of degrees of freedom. Another

important assumption is to assume that there is no correlation between cost reduction and quality

improvement trajectories. In this case it would be characterized a significant statistical “trade-

off” between adjustment strategies that can result in the deterioration of the performance

indicators of the regulated businesses.

By comparison models without dynamic terms have great significance. Thus, considering the

effect of improving the quality of the costs it is noted that the signs of the estimators are correct

and that from them it is possible to estimate the elasticity of substitution between costs and

improving quality indicators, without however correlate this impact with cost trajectories. Thus

the assumptions of the analyzes that follow are based on the assumption that costs are related to

the quality only, regardless of the effects of the derived cost savings from implementation of

other components of the X – factor, especially the T component that affects OPEX causing

effects on the estimates of the adjustments relative to DEC.

To compare the empirical estimates to the results of the amounts recognized by ANEEL, it takes

the average participation of the CAPEX on the distributors in Brazil, weighted by TOTEX

between 2005 and 2012 amounting to 47.5%. On this case the elasticities for the estimators of the

FEC will be multiplied by this term to reflect the impact of CAPEX on total costs. Comparisons

were made with models 9 and 13:

DEC

Variation Q ANEEL -

Blue

Q ANEEL -

Green

Q ANEEL -

Red

Q ANEEL -

Purple

Model don’t meet the

pattern

Model meet the

pattern

-20% -1,67 -1,04 -0,90 -1,00 -6,85 -7,33

-15% -1,33 -0,78 -0,68 -0,83 -5,13 -5,62

-10% -1,00 -0,52 -0,45 -0,67 -3,42 -3,91

-5% -0,67 -0,26 -0,23 -0,50 -1,71 -2,19

0% -0,33 0,00 0,00 -0,33 0,00 -0,48

5% 0,00 0,26 0,23 -0,17 1,71 1,23

10% 0,13 0,60 0,64 0,00 3,42 2,94

15% 0,27 0,90 0,96 0,33 5,13 4,65

20% 0,40 1,20 1,28 0,67 6,85 6,36

FEC

Variation Q ANEEL -

Blue

Q ANEEL -

Green Q ANEEL - Red

Q ANEEL -

Purple

Model don’t meet

the pattern

Model meet the

pattern

-20% -1,67 -1,04 -0,90 -1,00 -4,37 -5,12

-15% -1,33 -0,78 -0,68 -0,83 -3,28 -4,02

-10% -1,00 -0,52 -0,45 -0,67 -2,19 -2,93

-5% -0,67 -0,26 -0,23 -0,50 -1,09 -1,84

0% -0,33 0,00 0,00 -0,33 0,00 -0,74

5% 0,00 0,26 0,23 -0,17 1,09 0,35

10% 0,13 0,60 0,64 0,00 2,19 1,44

15% 0,27 0,90 0,96 0,33 3,28 2,54

20% 0,40 1,20 1,28 0,67 4,37 3,63

In the tables above we see that the models estimated by ANEEL recognized – under the analysis

of assumptions adopted without the inertial term – less costs than would be appropriate for the

improvement of quality indicators.

Nevertheless it is noted that both, the empirical reward, and the punishment for breaches of the

quality goals are greatly increased. For the most feasible performances between -5% and 5%

strong differences are noted. It is believed in this case that the regulatory incentive is undersized.

6. CONCLUSIONS

This paper demonstrated that the regulatory incentive to improve the quality of service provided

by power distribution utilities in Brazil can be changed in order to provide greater penalties or

incentives to improve the quality of service.

The principle of regulatory parsimony can make little regulatory feasible volatilities estimated

empirically, when it applies the tariff recognition of companies, either by volatility in tariffs

factors, whether the eventual extraction of the tariff increases for tariff setting methodologies.

It is noteworthy that the proposed results are not limited to the treatment of other regulatory

incentives involving a reduction in operating costs, which are ultimately important sources of

“trade-offs” particularly in the case of DEC.

It is characterized unless the restrictions of statistically significant model used to analyze the

regulatory incentive introduced in Brazil to improve quality indicators is not enough to “offset”

the expenses incurred by the distributors in improving the technical quality indicators in the short

term.

In the long run it is important to point it out that the regulatory incentive to improve the quality

must tend to be zero, “backed the operational characteristics of the companies and exogenous

factors that affect the quality such as the weather.” This is important as there are operators that

can serve as a reference for others in more mature concession areas. The concessions tend

dynamically become saturated when its quality index going to stationary state.

Thus the benchmark of quality is not actually bad, but it needs to capture the short-term needs of

the less mature concession areas, especially where there is still much work to be done for yours

difficulties. The economic and financial balance of concessions can’t be threatened by

performance awards that do not face the same short-term improvement challenges.

7. CONFLICTS OF INTEREST

The opinions described on this academic work don’t represent the institutional view of

ENERGISA Group, shareholders, managers and controllers.

8. BIBLIOGRAPHY

ANEEL: Audiência pública 023/2014 – revisão das metodologias para o 4CRTP :

http://www.aneel.gov.br/aplicacoes/audiencia/dspListaDetalhe.cfm?attAnoAud=2014&attIdeFas

Aud=938&id_area=13&attAnoFasAud=2015 Acessed at 13-12-2015.

ANEEL: PRORET http://www.aneel.gov.br/area.cfm?idArea=702 Acessed at 13-12-2015.

ANEEL: PRODIST http://www.aneel.gov.br/area.cfm?idArea=82 Acessed at 13-12-2015.

ARELLANO, M. BOND, S. - “Some Tests of Specification for Panel Data: Monte Carlo

Evidence and an Application to Employment Equations”, Review of Economic Studies, 1991.

A. C. CAMERON AND P. K. TRIVEDI – “Microeconometrics Using STATA” – Revised

Edition, Stata pres, 2010.

GREENE, W. - “Econometric Analysis”, 5ª ed., NJ: Upper Saddle River, Prentice Hall, 2002

HANSEN, L. P. - “Large Sample Proprieties of Generalized Method of Moments Estimators” –

Econometrica, 1982.

WOOLDRIDGE, J. M. – “Introdução à Econometria”, Ed. Thomson, 2007.